Although DRL has shown its advantages of performance and complexity compared to the traditional optimization approaches, there exist two main drawbacks for the existing DRL-based algorithms proposed in [1]}, [2]}, [3]}, [4]}, [5]}, [6]} on addressing the decision-making problems in the real-time V2X communications. First, DQN is used as the main technique to handle the discrete-continuous hybrid action space by discretizing continuous actions, which causes quantization error and degrades the performance since the output of DQN relies on the selection of the best action. In addition, the high dimensional quantization on the continuous action space will cause an exponential increase of the computational complexity. Second, the current DRL-based algorithms are designed based on the assumption that there is no change between the training and testing environments. However, such an assumption is impractical in V2X communications scenarios due to the high mobility and dynamic features of the vehicular environment. As a result, the existing DRL-based algorithm may cause the mismatch issue when the environment changes, which means such algorithms cannot make the right decision rapidly in the dynamic environment. These two challenges have not been solved and they restrict the development of efficient resource allocation methods in V2X communications.
